Row 1

The Global South continues to be underrepresented in behavioral science

First authors in behavioral science from…

Row 2

Representativeness of First Authors in Behavioral Science

The majority of the world’s population comes from outside the Global North. Yet this majority only represents a small fraction of first authors in behavioral science research, who tend to be located in North America or Europe, and are mostly based in the US (Thalmayer et al., 2021, Arnett, 2008). Thus, most of the world, and especially Africa, Latin America, and Asia, are underrepresented, which could affect the validity and generalizability of psychological research. This dashboard presents some aggregated data by continent, country, year, and journal (for first authors only), to better document this trend over time and, possibly, inform future public policy on the matter. Note: This dashboard is a work in progress (version Alpha).

If this matters to you, please reach out.

How to cite this dashboard?

Thériault, R., & Forscher, P. (2024). The Missing Majority in Behavioral Science Dashboard. https://remi-theriault.com/dashboards/missing_majority

Choice of Journals

The data from this report originally included information about publications from six psychology journals (Developmental Psychology, Journal of Personality and Social Psychology, Journal of Abnormal Psychology, Journal of Family Psychology, Health Psychology, and Journal of Educational Psychology), for years 1987 to 2023.

These journals were initially selected based on Arnett and colleagues’ papers. The dashboard now includes many more behavioral science journals, which were selected by soliciting input from colleagues to broadly represent the interdisciplinary field of behavioral science and the fields that contribute to it (i.e., psychology and economics). You can see the full list of journals on the Methods tab. If you think a journal should be there and it’s not, please open a Github issue and we’ll add it.

This dashboard was created with the pubDashboard package in R: https://rempsyc.github.io/pubDashboard/.

About the Authors

Rémi Thériault is currently a PhD candidate in Psychology at the Université du Québec à Montréal, Canada. Overall, Rémi is passionate about putting social-psychological research to use to increase people’s well-being and intrinsic motivation to help one another. He is also passionate about programming with R :)

Patrick Forscher is the primary collaborator on this project. He is currently the Director of the Culture, Research Ethics, and MEthods (CREME) Meta-Research Team at the Busara Center for Behavioral Economics. The dashboard was Patrick’s original vision, and frequently benefits from his creative input.

Busara is the dashboard’s sponsor. Busara works with researchers and organizations to advance and apply behavioral science in pursuit of poverty alleviation. They use behavioral science to design solutions for partner organisations that are working to make lives better in the Global South.

Continent

Row 1

Waffle plot of journal paper percentages, by continent (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Year

Row 1

Scatter plot of journal paper percentages, by continent and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Hovering over a plotly figure opens the Chart Studio Modebar on the top-right. It includes: (a) Download Plot as a PNG; (b) Zoom and Pan Buttons; (c) Zoom In/Out; (d) Autoscale and Reset Axes; and (e) Hover Options.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Journal

Row 1

Waffle plot of journal paper percentages, by continent and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country

Row 1

Waffle plot of journal paper percentages, by country (each flag = 1% of data)

Row 2

Table of journal paper percentages, by country

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Year

Row 1

Scatter plot of journal paper percentages, by country and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by country and year

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Journal

Row 1

Waffle plot of journal paper percentages, by country and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by country and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Journal, by Year

Row 1

Scatter plots of journal paper percentages, by journal, continent, and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent

Row 1

Waffle plot of journal paper percentages, by continent (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Year

Row 1

Scatter plot of journal paper percentages, by continent and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Journal

Row 1

Waffle plot of journal paper percentages, by continent and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country

Row 1

Waffle plot of journal paper percentages, by country (each flag = 1% of data)

Row 2

Table of journal paper percentages, by country

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Year

Row 1

Scatter plot of journal paper percentages, by country and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by country and year

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Journal

Row 1

Waffle plot of journal paper percentages, by continent and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by country and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Journal, by Year

Row 1

Scatter plots of journal paper percentages, by journal, continent, and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent

Row 1

Waffle plot of journal paper percentages, by continent (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Year

Row 1

Scatter plot of journal paper percentages, by continent and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Journal

Row 1

Waffle plot of journal paper percentages, by continent and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country

Row 1

Waffle plot of journal paper percentages, by country (each flag = 1% of data)

Row 2

Table of journal paper percentages, by country

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Year

Row 1

Scatter plot of journal paper percentages, by country and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by country and year

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Journal

Row 1

Waffle plot of journal paper percentages, by continent and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by country and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Journal, by Year

Row 1

Scatter plots of journal paper percentages, by journal, continent, and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent

Row 1

Waffle plot of journal paper percentages, by continent (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Year

Row 1

Scatter plot of journal paper percentages, by continent and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Continent, by Journal

Row 1

Waffle plot of journal paper percentages, by continent and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by continent and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country

Row 1

Waffle plot of journal paper percentages, by country (each flag = 1% of data)

Row 2

Table of journal paper percentages, by country

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Year

Row 1

Scatter plot of journal paper percentages, by country and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by country and year

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Country, by Journal

Row 1

Waffle plot of journal paper percentages, by continent and journal (each square = 1% of data)

Row 2

Table of journal paper percentages, by country and journal

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Journal, by Year

Row 1

Scatter plots of journal paper percentages, by journal, continent, and year

Instructions for Figures

The scatter plots use the plotly package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Interacting With Data Points and Legend Groupings

When hovering over individual data points, you can see the raw data
When hovering over the regression line, you can see the predicted data
Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.

Row 2

Table of journal paper percentages, by continent

Instructions for Tables

All tables use the DT package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.

Here are some tips to make the most of these tables.

You can click on the top left to change the number of entries to show
You can change page by clicking on the bottom right area
You can sort by column by clicking on the column of your choice
You can search for specific values (for example journals or countries) by using the search bar at the top-right

Methods

Row 1

METHOD & DATA

The dashboard includes information about the articles (e.g., title, abstract) as well as on the authors, such as university of affiliation. We have obtained these data from OpenAlex using the OpenAlex API through the openAlexR package, which relies on the OpenAlex database. openAlexR provides information about the country of the authors, affiliation address, and institution.

The full current list of journals queried can be obtained through the following R code pubDashboard::journal_field$journal_abbr.

On the right, we can see the list of journals included in this dashboard following the above query. The table also includes the number of publication for each journal; this is our sample size of sort. These data helps us understand in part the quality of the OpenAlex data.

Note that the big general journals (PloS One, Science, Nature, and PNAS) from the “General Interest” tab are only meant to act as a point of reference and are not included on the home page gauge data.

FAQ

1. Which affiliation is considered when the first-author has more than one?

When the first-author has several affiliations, only the first affiliation is considered for determining country and continent.

2. How do you account for papers that involve collaborations between researchers from both the Global North and Global South?

Currently, we only consider the first affiliation of the first author. However, it would be possible in a future release to include a “continent diversity” score calculating the total number of unique continent per paper to better account for international collaborations.

3. How do you separate authors’ current affiliation versus their country of origin?

We can only work with the data available to us through OpenAlex. OpenAlex only gives information about the authors current affiliation for a given paper, so it is not easily possible to know the country of origin of the authors. However, we agree that this is an important point given that successful researchers from the Global South may move to work at Global South research institutions for a variety of reasons.

JOURNAL COUNT

Limitations

Row 1

LIMITATIONS

This dashboard should be considered with the following limitations in mind:

1. The dashboard data are not per capita (yet)

This could bias our estimations since we do not look at relative ratios.

2. The way in which we define which journals represent behavioral science as a whole may be problematic

Authors from non-English-speaking countries (including in the Global South, and especially Latin America) are more likely to publish in languages other than English (like Spanish or French journals), which are less well-known. Saying that only English-speaking journals truly represent “Behavioral Science” as a whole is somewhat problematic. Would that mean that all the Spanish and French behavioral science journals do not they represent behavioral science as well, even though English monolinguals cannot read them?

3. The dashboard has lots of missing data, which is probably not random

Universities from the Global South and non-English speaking countries are less likely to be correctly detected, for example because of special characters in names or less well-known universities not included in our database. As a result, the country for these publications is more likely to be marked as missing and therefore not be included in the dashboard. This can further under-represent the Global South in our data.

More specifically, some of the papers were missing address information; in many cases, the API provided only the department and no university. It was not possible to identify the country in these cases (one would need to look at the actual papers one by one to make manual corrections). Furthermore, some university names from the data did not match the university name database obtained from GitHub. In some cases, I have brought manual corrections to university names in an attempt to reduce the number of missing values. A table of data with missing countries is accessible at the Missing Data tab.

4. First-authorship may misrepresent author credit in economics

Much of economics alphabetizes multi-author papers, though the norm is not constant across the field. This inconsistency makes it harder to interpret first-authorship for economics journals for this dashboard.

NEXT STEPS

Possible future steps include: (a) obtaining a better, more current university name database (that includes country of university), (b) making manual corrections for other research institutes not included in the university database, (c) host DT tables on a server to speed up the website and allow the inclusion of a DT table for exploring the raw data, (d) find a way to use country flags for the countries-by-journal figure, and (e) use per-capita data to make it more representative.

Missing Data

Row 1

This table allows investigating why the country/university could not be identified

Important Note

This data table is too large to display online in full

Initially, this dashboard included every publication in which the country and continent could not be identified. However, after adding large journals (Nature, Science, and PLOS One), this table became too large to display within the dashboard, making the webpage sluggish and slow to load. Therefore, only 1/1000 is preserved for now, until we start fixing the missing data by identifying universities and therefore reducing the overall size of the table. This should help speed up using the dashboard in other areas.

---
title: "The Missing Majority Dashboard"
author: 'Rémi Thériault'
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    social: menu
    source_code: embed
    # theme: lumen
    storyboard: false
    favicon: logo.ico
    css: style.css
---

<script>
   document.querySelector(".navbar-header").innerHTML =
            "<a href=\"#home\" class=\"navbar-brand navbar-inverse\">The Missing Majority Dashboard</a>";
</script> 

# Home {.hidden}

```{r setup, include=FALSE}
query_alex <- TRUE
# query_alex <- TRUE
options(scipen = 999)
```

```{r packages}
# Load packages
library(pubDashboard)
library(dplyr)
library(ggflags)

```

```{r save_process_batch_psychology, results='hide', eval=query_alex}
# We got a little problem with PLOS One, Science, Nature, and PNAS
# So we proceed separately for each...
lapply_fetch_openalex_pubs(
  over = "journal", journal_id = journal_field %>% 
    filter(field == "psychology") %>%
    pull(openalex_id),
  from_publication_date = "2024-01-01",
  file_suffix = "_new"
  )

# psychology <- fetch_openalex_pubs(
#   journal_id = journal_field %>%
#     filter(field == "psychology") %>% 
#     pull(openalex_id), 
#   from_publication_date = "2024-01-01",
#   clean_journals_continents = TRUE, 
#   progress_bar = TRUE)
# saveRDS(psychology, "data/data_psychology_new.rds")
```

```{r save_process_batch_economics, results='hide', eval=query_alex}
lapply_fetch_openalex_pubs(
  over = "journal", journal_id = journal_field %>% 
    filter(field == "economics") %>%
    pull(openalex_id),
  from_publication_date = "2024-01-01",
  file_suffix = "_new"
  )

# economics <- fetch_openalex_pubs(
#   journal_id = journal_field %>%
#     filter(field == "economics") %>% 
#     pull(openalex_id), 
#   from_publication_date = "2024-01-01",
#   clean_journals_continents = TRUE, 
#   progress_bar = TRUE)
# saveRDS(economics, "data/data_economics_new.rds")
```

```{r save_process_batch_pnas, results='hide', eval=query_alex}
PNAS <- fetch_openalex_pubs(
  journal_id = journal_field %>%
    filter(journal_abbr == "PNAS") %>% 
    pull(openalex_id), 
  from_publication_date = "2024-01-01",
  clean_journals_continents = TRUE, 
  progress_bar = TRUE)
saveRDS(PNAS, "data/data_PNAS_new.rds")
```

```{r save_process_batch_science, results='hide', eval=query_alex}
science <- fetch_openalex_pubs(
  journal_id = journal_field %>%
    filter(journal == "Science") %>% 
    pull(openalex_id), 
  from_publication_date = "2024-01-01",
  clean_journals_continents = TRUE, 
  progress_bar = TRUE)
saveRDS(science, "data/data_Science_new.rds")
```

```{r save_process_batch_nature, results='hide', eval=query_alex}
nature <- fetch_openalex_pubs(
  journal_id = journal_field %>%
    filter(journal == "Nature") %>% 
    pull(openalex_id), 
  from_publication_date = "2024-01-01",
  clean_journals_continents = TRUE, 
  progress_bar = TRUE)
saveRDS(nature, "data/data_Nature_new.rds")
```

```{r save_process_batch_plos, results='hide', eval=query_alex}
plosone <- fetch_openalex_pubs(
  journal_id = journal_field %>%
    filter(journal == "PloS One") %>% 
    pull(openalex_id), 
  from_publication_date = "2024-01-01",
  clean_journals_continents = TRUE, 
  progress_bar = TRUE)
saveRDS(plosone, "data/data_Plos One_new.rds")
```

```{r get_historic_data}
data <- read_bind_all_data()

# Correct year range
data <- data %>% 
  dplyr::group_by(.data$journal) %>%
    dplyr::mutate(first_Year = min(.data$year), last_year = max(.data$year),
           year_range = paste0(.data$first_Year, "-", .data$last_year)) %>%
    dplyr::ungroup()

# saveRDS(data, "data/fulldata.rds")

# We filter for year 1987 because there are almost no publications before that
data <- data %>%
  filter(year >= 1987)

# Temporarily reduce file
# data <- data %>%
#   filter(year >= 2022)
```

```{r stats_continent}
data_behavsci <- data %>% 
  filter(field != "general")

data_econ <- data %>% 
  filter(field == "economics")

data_psych <- data %>% 
  filter(field == "psychology")

data_gen <- data %>% 
  filter(field == "general")

stats_continent <- table_continent(data_behavsci, datatable = FALSE)
stats_country <- table_country(data_behavsci, datatable = FALSE)
```

## Row 1 {data-height=400}

### {data-width=1160}

[The Global South continues to be underrepresented in behavioral science]{.big_center}

### **First authors in behavioral science from...**

<!-- #### First authors in behavioral science from... -->

```{r}
rate <- round(stats_continent$`Latin America` + stats_continent$Africa)
flexdashboard::gauge(
  rate, 
  min = 0, 
  max = 100, 
  symbol = '%', 
  flexdashboard::gaugeSectors(
    danger = c(0, 40), 
    warning = c(40, 79), 
    success = c(80, 100)),
  abbreviateDecimals = 1, 
  href = "#continent",
  label = "Latin America & Africa"
)
```

<!-- #### From Latin America and Africa -->

```{r, eval=T}
rate <- stats_country %>% 
  filter(Country == "United States") %>% 
  pull(Percentage) %>% 
  round()
flexdashboard::gauge(
  rate, 
  min = 0, 
  max = 100, 
  symbol = '%', 
  flexdashboard::gaugeSectors(
    success = c(0, 40), 
    warning = c(40, 79), 
    danger = c(80, 100)),
  abbreviateDecimals = 1, 
  href = "#country",
  label = "USA"
)
```

<!-- #### From the US -->

```{r, eval=T}
rate <- round(stats_continent$`North America` + stats_continent$Europe)
flexdashboard::gauge(
  rate, 
  min = 0, 
  max = 100, 
  symbol = '%', 
  flexdashboard::gaugeSectors(
    danger = c(0, 40), 
    warning = c(40, 79), 
    success = c(80, 100)),
  abbreviateDecimals = 1, 
  href = "#continent",
  label = "North America & Europe"
)
```

<!-- #### From North America and Europe -->

## Row 2

### **Representativeness of First Authors in Behavioral Science**

The majority of the world’s population comes from outside the Global North. Yet this majority only represents a small fraction of first authors in behavioral science research, who tend to be located in North America or Europe, and are mostly based in the US ([Thalmayer et al., 2021](https://psycnet.apa.org/doi/10.1037/amp0000622), [Arnett, 2008](https://doi.org/10.1037/0003-066x.63.7.602)). Thus, most of the world, and especially Africa, Latin America, and Asia, are underrepresented, which could affect the validity and generalizability of psychological research. This dashboard presents some aggregated data by continent, country, year, and journal (for first authors only), to better document this trend over time and, possibly, inform future public policy on the matter. *Note:* This dashboard is a work in progress (version Alpha).

If this matters to you, please [reach out](https://www.busara.global/partner-with-us/).

---

**How to cite this dashboard?**

Thériault, R., & Forscher, P. (2024). *The Missing Majority in Behavioral Science Dashboard*. https://remi-theriault.com/dashboards/missing_majority

### **Choice of Journals**

The data from this report originally included information about publications from six psychology journals (*Developmental Psychology*, *Journal of Personality and Social Psychology*, *Journal of Abnormal Psychology*, *Journal of Family Psychology*, *Health Psychology*, and *Journal of Educational Psychology*), for years 1987 to 2023.

These journals were initially selected based on Arnett and colleagues' papers. The dashboard now includes many more behavioral science journals, which were selected by soliciting input from colleagues to broadly represent the interdisciplinary field of behavioral science and the fields that contribute to it (i.e., psychology and economics). You can see the full list of journals on the [Methods] tab. If you think a journal should be there and it's not, please open a [Github issue](https://github.com/rempsyc/busara_dashboard/issues/) and we'll add it.

---

*This dashboard was created with the `pubDashboard` package in R: https://rempsyc.github.io/pubDashboard/.*

### **About the Authors**

**[Rémi Thériault](https://remi-theriault.com/)** is currently a PhD candidate in Psychology at the Université du Québec à Montréal, Canada. Overall, Rémi is passionate about putting social-psychological research to use to increase people’s well-being and intrinsic motivation to help one another. He is also passionate about programming with R :)

**[Patrick Forscher](https://busaracenter.org/patrick-s-forscher/)** is the primary collaborator on this project. He is currently the Director of the Culture, Research Ethics, and MEthods (CREME) Meta-Research Team at the Busara Center for Behavioral Economics. The dashboard was Patrick's original vision, and frequently benefits from his creative input.

**[Busara](https://www.busara.global/)** is the dashboard's sponsor. Busara works with researchers and organizations to advance and apply behavioral science in pursuit of poverty alleviation. They use behavioral science to design solutions for partner organisations that are working to make lives better in the Global South.

```{r citations}
citation <- "Thériault, R., & Forscher, P. (2024). <br>_The Missing Majority in Behavioral Science Dashboard_. <br>https\\://remi-theriault.com/dashboards/missing_majority"

citation2 <- 'Thériault, R., & Forscher, P. (2024). <br><i>The Missing Majority in Behavioral Science Dashboard</i>. <br>https://remi-theriault.com/dashboards/missing_majority  '
```

```{r instructions_reminder_table}
instructions_figure <- "
### Instructions for Figures\n

####\n

The scatter plots use the [`plotly`](https://plotly.com/r/) package, which converts regular images into interactive web graphics via the open source JavaScript graphing library plotly.js.

Hovering over a plotly figure opens the [Chart Studio Modebar](https://plotly.com/chart-studio-help/getting-to-know-the-plotly-modebar/) on the top-right. It includes: (a) Download Plot as a PNG; (b) Zoom and Pan Buttons; (c) Zoom In/Out; (d) Autoscale and Reset Axes; and (e) Hover Options.\n

![](plotly_options.png){width=100%}

**Interacting With Data Points and Legend Groupings**\n

1. When hovering over individual data points, you can see the raw data
2. When hovering over the regression line, you can see the predicted data
3. Click, hold, and drag to zoom into a specific window of the chart. Double-click anywhere to come back to the original zoom level
4. Double-clicking on a group or line will isolate it. Double-clicking again will discontinue the isolation.
5. Single-clicking one or more groups or lines will remove them. Single-clicking again will bring them back.
"

instructions_table <- "
> \\* Percentages are calculated after excluding missing values. The *Missing* column shows the real percentage of missing values.\n

### Instructions for Tables\n

####\n

All tables use the [DT](https://rstudio.github.io/DT/) package, which converts regular dataframes into interactive datatable HTML table widgets via the open source JavaScript library DataTables.\n

Here are some tips to make the most of these tables.\n

1. You can click on the top left to change the number of entries to show
2. You can change page by clicking on the bottom right area
3. You can sort by column by clicking on the column of your choice
4. You can search for specific values (for example journals or countries) by using the search bar at the top-right\n

![](dt_table.png){width=100%}
"
```

```{r behavscience_parent, child='missing_majority_behavscience.Rmd', eval=T}
```

```{r psych_parent, child='missing_majority_psychology.Rmd', eval=T}
```

```{r econ_parent, child='missing_majority_economics.Rmd', eval=T}
```

```{r gen_parent, child='missing_majority_general.Rmd', eval=T}
```

# Methods

## Row 1 {data-height=700}

### **METHOD & DATA**

The dashboard includes information about the articles (e.g., title, abstract) as well as on the authors, such as university of affiliation. We have obtained these data from OpenAlex using the OpenAlex API through the `openAlexR` package, which relies on the [OpenAlex database](https://openalex.org/). `openAlexR` provides information about the country of the authors, affiliation address, and institution.

The full current list of journals queried can be obtained through the following R code `pubDashboard::journal_field$journal_abbr`.

On the right, we can see the list of journals included in this dashboard following the above query. The table also includes the number of publication for each journal; this is our sample size of sort. These data helps us understand in part the quality of the OpenAlex data.

**Note that the big general journals (PloS One, Science, Nature, and PNAS) from the "General Interest" tab are only meant to act as a point of reference and are not included on the home page gauge data.**

#### FAQ

**1. Which affiliation is considered when the first-author has more than one?**

When the first-author has several affiliations, only the first affiliation is considered for determining country and continent.

**2. How do you account for papers that involve collaborations between researchers from both the Global North and Global South?**

Currently, we only consider the first affiliation of the first author. However, it would be possible in a future release to include a "continent diversity" score calculating the total number of unique continent per paper to better account for international collaborations.

**3. How do you separate authors' current affiliation versus their country of origin?**

We can only work with the data available to us through OpenAlex. OpenAlex only gives information about the authors current affiliation for a given paper, so it is not easily possible to know the country of origin of the authors. However, we agree that this is an important point given that successful researchers from the Global South may move to work at Global South research institutions for a variety of reasons.

### **JOURNAL COUNT**

```{r, journal_count}

table_journal_count(data = data)

```

# Limitations {data-navmenu="Limitations"}

## Row 1 {data-height=700}

### **LIMITATIONS**

This dashboard should be considered with the following limitations in mind:

**1. The dashboard data are not per capita (yet)**

This could bias our estimations since we do not look at relative ratios.

**2. The way in which we define which journals represent behavioral science as a whole may be problematic**

Authors from non-English-speaking countries (including in the Global South, and especially Latin America) are more likely to publish in languages other than English (like Spanish or French journals), which are less well-known. Saying that only English-speaking journals truly represent "Behavioral Science" as a whole is somewhat problematic. Would that mean that all the Spanish and French behavioral science journals do not they represent behavioral science as well, even though English monolinguals cannot read them?

**3. The dashboard has lots of missing data, which is probably not random**

Universities from the Global South and non-English speaking countries are less likely to be correctly detected, for example because of special characters in names or less well-known universities not included in our database. As a result, the country for these publications is more likely to be marked as missing and therefore not be included in the dashboard. This can further under-represent the Global South in our data.

More specifically, some of the papers were missing address information; in many cases, the API provided only the department and no university. It was not possible to identify the country in these cases (one would need to look at the actual papers one by one to make manual corrections). Furthermore, some university names from the data did not match the university name database obtained from GitHub. In some cases, I have brought manual corrections to university names in an attempt to reduce the number of missing values. A table of data with missing countries is accessible at the [Missing Data] tab.

**4. First-authorship may misrepresent author credit in economics**

Much of economics alphabetizes multi-author papers, though the norm is not constant across the field. This inconsistency makes it harder to interpret first-authorship for economics journals for this dashboard.

### **NEXT STEPS**

Possible future steps include: (a) obtaining a better, more current university name database (that includes country of university), (b) making manual corrections for other research institutes not included in the university database, (c) host DT tables on a server to speed up the website and allow the inclusion of a DT table for exploring the raw data, (d) find a way to use country flags for the countries-by-journal figure, and (e) use per-capita data to make it more representative.

# Missing Data {data-navmenu="Limitations"}

## Row 1 {.tabset .tabset-fade}

### This table allows investigating why the country/university could not be identified {data-height=700}

```{r missing_universities, warning=FALSE}
divided <- 1000
data %>% 
  slice(1:(nrow(.) / divided)) %>% 
  table_missing_country()

```

### Important Note

**This data table is too large to display online in full**

Initially, this dashboard included every publication in which the country and continent could not be identified. However, after adding large journals (Nature, Science, and PLOS One), this table became too large to display within the dashboard, making the webpage sluggish and slow to load. Therefore, only 1/`r divided` is preserved for now, until we start fixing the missing data by identifying universities and therefore reducing the overall size of the table. This should help speed up using the dashboard in other areas.